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<Paper uid="A97-2002">
  <Title>An Efficient Two Stage Approach to Robust Language Interpretation</Title>
  <Section position="2" start_page="0" end_page="3" type="metho">
    <SectionTitle>
2 Comparison to Alternative
Approaches
</SectionTitle>
    <Paragraph position="0"> Rather than placing the full burden of robustness on the parser itself, I argue that it is more economical for Partial Parsing and Combination to be separate steps in the Hypothesis Formation stage. Efforts towards solving the problem of extragrammatieality have primarily been in the direction of building flexible parsers. In principle, Minimum Distance Parsers (Lehman, 1989; Hipp, 1992) have the greatest flexibility. They fit an extragrammatical sentence to the parsing grammar through a series of insertions, deletions, and transpositions. Since any string can be mapped onto any other string through a series of insertions, deletions, and transpositions, this approach makes it possible to perform any desired repair. The underlying assumption behind the MDP approach is that the analysis of the string which deviates the least from the input string is most likely  to be the best analysis. Thus, Minimum Distance Parsing appears to be a reasonable approach.</Paragraph>
    <Paragraph position="1"> In practice, however, Minimum Distance Parsing has only been used successfully in very small and limited domains. Lehman's core grammar, described in (Lehman, 1989), has on the order of 300 rules, and all of the inputs to her system can be assumed to be commands to a calendar program. Hipp's Circuit Fix-It Shop system, described in (Hipp, 1992), has a vocabulary of only 125 words and a grammar size of only 500 rules. Flexible parsing algorithms introduce a great deal of extra ambiguity. This in turn may deem certain approaches impractical for systems of realistic scale. Therefore, an important question one must ask is whether the MDP approach can scale up to a larger system and/or domain.</Paragraph>
    <Paragraph position="2"> An example of a less powerful parsing algorithm is Lavie's GLR* skipping parser described in (Lavie, 1995). This parser is capable of skipping over any portion of an input utterance that cannot be incorporated into a grammatical analysis and recover the analysis of the largest grammatical subset of the utterance. Partial analyses for skipped portions of the utterance are also returned by the parser. Thus, whereas MDP considers insertions and transpositions in addition to deletions, GLR* only considers deletions. The weakness of this and other partial parsing approaches (Abney, 1997; Nord, 1997; Srinivas et al., 1997; Federici, Montemagni, and Pirrelli, 1997) is that part of the original meaning of the utterance may be thrown away with the portion(s) of the utterance which are skipped if only the analysis for the largest subset is returned, or part of the analysis will be missing if the parser only attempts to build a partial parse. These less powerful algorithms trade coverage for speed. The idea is to introduce enough flexibility to gain an acceptable level of coverage at an acceptable computational expense.</Paragraph>
    <Paragraph position="3"> The goal behind ROSE and other two stage approaches (Ehrlich and Hanrieder, 1997; Danieli and Gerbino, 1995) is to increase the coverage possible at a reasonable computational cost by introducing a post-processing repair stage, which constructs a complete meaning representation out of the fragments of a partial parse. Since the input to the second stage is a collection of partial parses, the additional flexibility that is introduced at this second stage can be channeled just to the part of the analysis that the parser does not have enough knowledge to handle straightforwardly. This is unlike the MDP approach, where the full amount of flexibility is unnecessarily applied to every part of the analysis. Therefore, this two stage process is more efficient since the first stage is highly constrained by the grammar and the results of this first stage are then used to constrain the search in the second stage.</Paragraph>
    <Paragraph position="4"> Additionally, in cases where the limited flexibility parser is sufficient, the second stage can be entirely bypassed, yielding an even greater savings in time.</Paragraph>
  </Section>
  <Section position="3" start_page="3" end_page="4" type="metho">
    <SectionTitle>
3 A Simple Example
</SectionTitle>
    <Paragraph position="0"> The heart of the ROSE approach is the Combination Mechanism, a genetic programming (Koza, 1992; Koza, 1994) environment in which programs are evolved which combine the fragments of a partial parse into a complete meaning representation structure. I present a simple example in Figure 1 for the sake of clarity. This should not be taken to be an indication of the full potential of this approach.</Paragraph>
    <Paragraph position="1">  The ideal repair hypothesis for this example is one  that specifies that the temporal expression should be inserted into the when slot in the *busy frame. Other hypotheses are also evolved and tested as the genetic programming system runs, such as the alternative example included in Figure 1. A fitness function ranks hypotheses, narrowing down on a small set.</Paragraph>
    <Paragraph position="2"> The final result is selected through interaction with the user.</Paragraph>
  </Section>
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